The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
Open accessAbstract
Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Using the common intra-site DEGs to validate the called inter-site DEGs, the MCC41, 42 showed that PEER also yields a higher accuracy than the original limmavoom method (Supplementary Fig. 22d,e), indicating that global data normalization analysis methods such as PEER can also be used to improve RNA-seq data across both Illumina and the PGM platforms.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [42]
In the citing paper
“for instance, the Food and Drug Administration (FDA) agency of the USA employed the MCC as the main evaluation measure in the MicroArray II / Sequencing Quality Control (MAQC/SEQC) projects [41, 42].”
From The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (2020) · cites this paper as [41]
In the citing paper
“Within the MicroArray Quality Control (MAQC)-II study [7], we observed, however, that the performance of gene expression models in predicting clinical outcome was limited and largely dependent on the respective clinical endpoint.”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
In the citing paper
“Within the MicroArray Quality Control (MAQC)-II study [7], we observed, however, that the performance of gene expression models in predicting clinical outcome was limited and largely dependent on the respective clinical endpoint.”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
In the citing paper
“Considering the fact that the nature of the clinical endpoint strongly affects a classifier’s performance [7, 18], we included endpoints that are known to cover a broad range of predictive difficulties (low difficulty, SEX, and CLASS LABEL; intermediate difficulty, EFS ALL, and OS ALL; high difficulty, EFS HR, and OS HR).”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
In the citing paper
“Evaluation of the prediction performances in the validation set revealed that the endpoint represents the most relevant factor affecting model performances, which is well in line with the findings of the MAQC-II study [7].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
Dudoit, S., Fridlyand, J., & Speed, T. P. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502753479248 · Cited by 2,671 · cited 1×
“Although previous studies have compared and benchmarked individual steps in the model development proces”
ResultMAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126
Self-citationThat BLUP is a Good Thing: The Estimation of Random Effects
Robinson, G. K. (1991) · Statistical Science · doi:10.1214/ss/1177011926 · Cited by 1,694
Selection bias in gene extraction on the basis of microarray gene-expression data
Ambroise, C., & McLachlan, G. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.102102699 · Cited by 1,537 · cited 1×
“, poor data quality and/or insufficient cross-validation of all model development step”
BackgroundDavé, S. S., Wright, G. W., Tan, B. K., Rosenwald, A., Gascoyne, R. D., Chan, W. C., Fisher, R. I., Braziel, R. M., Rimsza, L. M., Grogan, T. M., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041869 · Cited by 1,417 · cited 1×
“Previously, reanalysis of a widely cited single stud”
BackgroundMicroarray data analysis: from disarray to consolidation and consensus
Allison, D. B., Cui, X., Page, G. P., & Sabripour, M. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1749 · Cited by 1,339
Buyse, M. E., Loi, S., Veer, L. V., Viale, G., Delorenzi, M., Glas, A. M., Saghatchian, M., Bergh, J. C. S., Lidereau, R., Ellis, P., et al. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj329 · Cited by 1,238
Shedden, K., Taylor, J. M. G., Misek, D. E., Chang, A. C., Hanash, S., Kuick, R. D., Beer, D. G., Enkemann, S. A., Yeatman, T. J., Eschrich, S. A., et al. (2008) · Nature Medicine · doi:10.1038/nm.1790 · Cited by 1,176
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 894
Identification of early liver toxicity gene biomarkers using comparative supervised machine learning
Smith, B. P., Auvil, L. S., Welge, M. E., Bushell, C., Bhargava, R., Elango, N., Johnson, K. J., & Madak‐Erdogan, Z. (2020) · Scientific Reports · doi:10.1038/s41598-020-76129-8 · Cited by 32
Portraying the expression landscapes of cancer subtypes
Hopp, L., Loeffler‐Wirth, H., Fasold, M., & Binder, H. (2013) · Systems Biomedicine · doi:10.4161/sysb.25897 · Cited by 29
“A contest study of the Microarray Quality Control Consortium (MAQC) involving 17 teams of researchers showed that differences in proficiency between data analysis teams, especially in their experience levels, could strongly affect the quality of analysis results.(4) This finding emphasizes the need for easyto-use, int…”
BackgroundBudczies, J., Kosztyla, D., von Törne, C., Stenzinger, A., Drab-Esfahani, S., Dietel, M., & Denkert, C. (2014) · Journal of Statistical Software · doi:10.18637/jss.v059.i01 · Cited by 29
Ensemble analyses improve signatures of tumour hypoxia and reveal inter-platform differences
Fox, N. S., Starmans, M. H. W., Haider, S. A., Lambin, P., & Boutros, P. C. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-170 · Cited by 29
Calibration of medical diagnostic classifier scores to the probability of disease
Chen, W., Sahiner, B., Samuelson, F., Pezeshk, A., & Petrick, N. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280216661371 · Cited by 29
Self-citationKnight, J. M., Rizzo, J. D., Wang, T., He, N., Logan, B. R., Spellman, S. R., Lee, S. J., Verneris, M. R., Arevalo, J. M. G., & Cole, S. W. (2019) · JNCI Cancer Spectrum · doi:10.1093/jncics/pkz073 · Cited by 28
“5–25 kg/m(2)) 85 (33) Overweight (25–30 kg/m(2)) 83 (32)”
Jabato, F. M., Caballero, J., Rojano, E., Mateo, C. R., Sanz, P., Pérez, B., Gallego, D., Seoane, P., Ranea, J. A. G., & Perkins, J. R. (2021) · Scientific Reports · doi:10.1038/s41598-021-94343-w · Cited by 28
Mishra, S., Shaw, K., Mishra, D., Patil, S., Kotecha, K. V., Kumar, S., & Bajaj, S. K. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.858282 · Cited by 38
“Normalization has been used on the dataset to improve accuracy and avoid the model’s biasing (40–47).”
MethodDNA microarray-based gene expression profiling of estrogenic chemicals
Kiyama, R., & Zhu, Y. (2014) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-013-1544-5 · Cited by 29
A machine learning pipeline for quantitative phenotype prediction from genotype data
Guzzetta, G., Jurman, G., & Furlanello, C. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-s8-s3 · Cited by 27
Magis, A. T., & Price, N. D. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-227 · Cited by 28
Systems biology approaches to finding novel pain mediators
Antunes‐Martins, A., Perkins, J. R., Lees, J., Hildebrandt, T., Orengo, C. A., & Bennett, D. (2012) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1192 · Cited by 29
Actions of Huangqi decoction against rat liver fibrosis: a gene expression profiling analysis
Zhang, G.-B., Song, Y., Chen, Q., Dong, S., Lu, Y.-Y., Su, M.-Y., Liu, P., & Su, S.-B. (2015) · Chinese Medicine · doi:10.1186/s13020-015-0066-5 · Cited by 28
Liu, Y., Jing, R., Xu, J., Liu, K., Xue, J., Wen, Z., & Li, M. (2015) · Biomarkers in Medicine · doi:10.2217/bmm.15.97 · Cited by 27
Xiong, T., & Luo, Z. (2018) · Medical Science Monitor · doi:10.12659/msm.908944 · Cited by 27
BNP-Seq: Bayesian Nonparametric Differential Expression Analysis of Sequencing Count Data
Dadaneh, S. Z., Qian, X., & Zhou, M. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1328358 · Cited by 26
Oshi, M., Kawaguchi, T., Yan, L., Peng, X., Qi, Q., Tian, W., Schulze, A. K., McDonald, K.-A., Narayanan, S., Young, J. S., et al. (2021) · PubMed · Cited by 28
Cheng, T., Hu, C., Yang, H., Cao, L., & An, J. (2014) · International Journal of Oncology · doi:10.3892/ijo.2014.2623 · Cited by 27
ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences
Yang, W., Rosenstiel, P. C., & Schulenburg, H. (2016) · BMC Genomics · doi:10.1186/s12864-016-2848-2 · Cited by 28
Zahoor, J., & Zafar, K. (2020) · Genes · doi:10.3390/genes11070819 · Cited by 27
The Promise of Neurotechnology in Clinical Translational Science
White, S. W., Richey, J. A., Gračanin, D., Bell, M. A., LaConte, S. M., Coffman, M. C., Wieckowski, A. T., & Kim, I. (2014) · Clinical Psychological Science · doi:10.1177/2167702614549801 · Cited by 28
“The next frontier of data analysis of simultaneous EEG-fMRI recordings goes beyond asymmetrical integration to data fusion and cross-platform analysis (Huster et al., 2012; Meyer et al., 2013; Shi et al, 2010; Villinger, Mulert, & Lemieux, 2010; Warnat et al, 2005).”
Mercer, T. R., Xu, J., Mason, C. E., & Tong, W. (2021) · Genome biology · doi:10.1186/s13059-021-02528-3 · Cited by 27
“The founding project, MAQC Phase I, was initiated in 2005 by the FDA’s National Center for Toxicological Research (NCTR) to evaluate the reliability of microarray technologies that were being increasingly used in research, clinical diagnosis, and drug development and thus posed an urgency for the FDA to address the re…”
The role of gene expression profiling in drug discovery
Bates, S. (2011) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2011.06.009 · Cited by 26
Alhussaini, A. J., Steele, J. D., Jawli, A., & Nabi, G. (2024) · Cancers · doi:10.3390/cancers16081454 · Cited by 26
“MCC is an effective solution to overcome class imbalance and has been applied by the Food and Drug Administration (FDA) agency in the USA for the evaluation of MicroArrayII/Sequencing Quality control (MAQC/SEQC) projects [52].”
MethodZeng, Y., Wang, G., Zhou, C., Zhang, H., Sun, H., Zhang, W., Zhou, H., Liu, R., & Zhu, Y. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00574 · Cited by 26
GSDME-mediated pyroptosis promotes anti-tumor immunity of neoadjuvant chemotherapy in breast cancer
Fu, C., Ji, W., Cui, Q., Chen, A., Weng, H., Lu, N., & Yang, W. (2024) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-024-03752-z · Cited by 25
“After extracting gene expression data and excluding all samples with incomplete data, a total of 744 patients from GSE32646 [21], GSE20271 [22], GSE20194 [23, 24], GSE25055 [25], and GSE41998 [26] were included in this study (Supplemental Table 1).”
MethodKallah-Dagadu, G., Mohammed, M., Nasejje, J. B., Mchunu, N. N., Twabi, H. S., Batidzirai, J. M., Singini, G. C., Nevhungoni, P., & Maposa, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-85323-5 · Cited by 28
Infrared imaging of MDA-MB-231 breast cancer cell line phenotypes in 2D and 3D cultures
Smolina, M., & Goormaghtigh, E. (2014) · The Analyst · doi:10.1039/c4an01833h · Cited by 24
Oshi, M., Angarita, F. A., Tokumaru, Y., Yan, L., Matsuyama, R., Endo, I., & Takabe, K. (2021) · Cancers · doi:10.3390/cancers13102401 · Cited by 25
Impact of gene annotation choice on the quantification of RNA-seq data
Chisanga, D., Liao, Y., & Shi, W. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04644-8 · Cited by 32
“The RNA-seq data used for evaluation in this study are a benchmark dataset generated by the Sequencing Quality Control (SEQC) project [1], the third stage of the MicroArray Quality Control (MAQC) study [13, 14].”
MethodInayatullah, M., Mahesh, A., Turnbull, A., Dixon, J. M., Natrajan, R., & Tiwari, V. K. (2024) · EMBO Molecular Medicine · doi:10.1038/s44321-024-00050-0 · Cited by 28
Multidimensionality of microarrays: Statistical challenges and (im)possible solutions
Michiels, S., Kramar, A., & Koscielny, S. (2011) · Molecular Oncology · doi:10.1016/j.molonc.2011.01.002 · Cited by 25
“…II, led by the FDA has recently shown that variations on univariate gene selection methods and prediction rules have only a modest impact on performance (Shi et al., 2010) and several statistically equally good predictors can be developed for any given classification problem (Popovici et al., 2010).”
BackgroundPerceptron ensemble of graph-based positive-unlabeled learning for disease gene identification
Jowkar, G.-H., & Mansoori, E. G. (2016) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2016.07.004 · Cited by 25
Soliman, H. H., Wagner, S., Flake, D. D., Robson, M. E., Schwartzberg, L. S., Sharma, P., Magliocco, A. M., Kronenwett, R., Lancaster, J. M., Lanchbury, J. S. S., et al. (2020) · Annals of Surgical Oncology · doi:10.1245/s10434-019-08039-7 · Cited by 25
TinderMIX: Time-dose integrated modelling of toxicogenomics data
Serra, A., Fratello, M., del Giudice, G., Saarimäki, L. A., Paci, M., Federico, A., & Greco, D. (2020) · GigaScience · doi:10.1093/gigascience/giaa055 · Cited by 25
“In particular, transcriptomics technologies allow the investigation of gene expression patterns of thousands of genes after exposure to a substance [3] and they have been widely used in toxicogenomics [4–7].”
The dantzig selector for censored linear regression models
Li, Y., Dicker, L. H., & Zhao, S. D. (2012) · Statistica Sinica · doi:10.5705/ss.2011.220 · Cited by 25
“We retrieved the data from the MicroArray Quality Control Consortium II (Shi et al., 2010) GEO entry (GSE24080).”
MethodFactors affecting the accuracy of a class prediction model in gene expression data
Novianti, P. W., Jong, V. L., Roes, K. C. B., & Eijkemans, M. J. C. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0610-4 · Cited by 24
Bringing RNA-seq closer to the clinic
Keuren‐Jensen, K. R. V., Keats, J. J., & Craig, D. W. (2014) · Nature Biotechnology · doi:10.1038/nbt.3017 · Cited by 24
Lessons learned in the analysis of high-dimensional data in vaccinomics
Oberg, A. L., McKinney, B. A., Schaid, D. J., Pankratz, V. S., Kennedy, R. B., & Poland, G. A. (2015) · Vaccine · doi:10.1016/j.vaccine.2015.04.088 · Cited by 26
“Most classifiers can yield competitive classification performance if sound internal model validation practices such as cross validation are utilized to provide honest estimates of model performance[14, 61].”
BackgroundThe FDA’s Experience with Emerging Genomics Technologies—Past, Present, and Future
Xu, J., Thakkar, S., Gong, B., & Tong, W. (2016) · The AAPS Journal · doi:10.1208/s12248-016-9917-y · Cited by 25
“The entire project published a total of 28 peer-reviewed articles (http://www.fda.gov/ScienceResearch/BioinformaticsTools/ MicroarrayQualityControlProject/), and 11 of them were published in Nature Biotechnology (4, 16-24).”
BackgroundMezencev, R., & Subramaniam, R. P. (2019) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2019.114706 · Cited by 24
Sorokin, M., Ignatev, K., Barbara, V., Vladimirova, U., Muraveva, A., Suntsova, M., Гайфуллин, Н., Vorotnikov, I. K., Kamashev, D., Bondarenko, A., et al. (2020) · Biochemistry (Moscow) · doi:10.1134/s0006297920070044 · Cited by 24
Systems-level differential gene expression analysis reveals new genetic variants of oral cancer
Abbas, S. Z., Qadir, M. I., & Muhammad, S. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-71346-7 · Cited by 24
Jiang, J., Peng, Z., Wang, J., Chen, M., Wan, Y., Huang, H., Liu, Z., Wang, J., & Hou, J. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-007593 · Cited by 24
The age-specific comorbidity burden of mild cognitive impairment: a US claims database study
Li, G., Toschi, N., Devanarayan, V., Batrla, R., Boccato, T., Cho, M., Ferrante, M., Frech, F. H., Galvin, J. E., Henley, D. B., et al. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01358-8 · Cited by 25
Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand
Kohonen, P. J., Ceder, R., Smit, I. A., Hongisto, V., Myatt, G. J., Hardy, B. J., Spjuth, O., & Grafström, R. (2014) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/bcpt.12257 · Cited by 24
Classifier variability: Accounting for training and testing
Chen, W.-J., Gallas, B. D., & Yousef, W. A. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2011.12.024 · Cited by 24
Rao, A. N., Rodesch, C. K., & Grainger, D. W. (2012) · Analytical Chemistry · doi:10.1021/ac302165h · Cited by 26
Optimising parallel R correlation matrix calculations on gene expression data using MapReduce
Wang, S., Pandis, I., Johnson, D., Emam, I., Guitton, F., Oehmichen, A., & Guo, Y. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0351-9 · Cited by 24
Correlating in vitro data to in vivo findings for risk assessment.
Bale, A. S., Kenyon, E., Flynn, T., Lipscomb, J., Mendrick, D., Hartung, T., & Patton, G. W. (2014) · ALTEX · doi:10.14573/altex.1310011 · Cited by 24
“In the MAQC-II project (Shi et al., 2010) 36 expert teams evaluated 6 microarray datasets.”
BackgroundAdvances in machine learning for optimizing pharmaceutical drug discovery
Ogbonna, U. E., Ofoezie, E. F., Babalola, O. O., Ottu, P. O., Ogbonna, C. A., Olisakwe, S. C., George, E. T., Babarinde, S., Omaba, O. J., Chukwuemeka, C. G., et al. (2025) · Current Proteomics · doi:10.1016/j.curpro.2025.100015 · Cited by 25
Wolff, A., Bayerlová, M., Gaedcke, J., Kube, D., & Beißbarth, T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197162 · Cited by 23
Designing, Performing, and Interpreting a Microarray-Based Gene Expression Study
Coppola, G. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-328-8_28 · Cited by 23
Caboux, É., Paciencia, M., Durand, G., Robinot, N. R., Wozniak, M. B., Galateau-Sallé, F., Byrnes, G. B., Hainaut, P. L., & Calvez‐Kelm, F. L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079826 · Cited by 23
PLIDA: cross-platform gene expression normalization using perturbed topic models
Deshwar, A. G., & Morris, Q. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt574 · Cited by 23
Liu, R., Lv, Q., Yu, J., Hu, L., Zhang, L., Cheng, Y., & Zhou, H. (2015) · Breast Cancer Research and Treatment · doi:10.1007/s10549-015-3428-x · Cited by 23
Algebraic Comparison of Partial Lists in Bioinformatics
Jurman, G., Riccadonna, S., Visintainer, R., & Furlanello, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036540 · Cited by 23
Development and Validation of a 9-Gene Prognostic Signature in Patients With Multiple Myeloma
Liu, X., Yin, X., Meng, X., Yan, X., Wang, F., & Li, H. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2018.00615 · Cited by 22
“To avoid overfitting as much as possible, we conducted LASSO penalized Cox proportional hazards regression model in the training set in GSE24080, and the results identified 9 of the 325 hub genes (HLADPB1(major histocompatibility complex, class II, DP beta 1), TOP2A (topoisomerase 2A), FABP5 (Fatty Acid-Binding Protei…”
BackgroundInfluentialCirculating markers for prognosis of hepatocellular carcinoma
Wong, K.-F., Xu, Z., Chen, J., Lee, N. P. Y., & Luk, J. M. (2013) · Expert Opinion on Medical Diagnostics · doi:10.1517/17530059.2013.795146 · Cited by 22
Maximizing biomarker discovery by minimizing gene signatures
Chang, C., Wang, J., Zhao, C., Fostel, J. M., Tong, W., Bushel, P. R., Deng, Y., Pusztai, L., Symmans, W. F., & Shi, T. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-s5-s6 · Cited by 22
Pielaat, A., Barker, G. C., Hendriksen, P. J. M., Hollman, P. C. H., Peijnenburg, A., & ter Kuile, B. H. (2013) · EFSA Supporting Publications · doi:10.2903/sp.efsa.2013.en-495 · Cited by 22
Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data
Kim, H., Kim, Y., Han, B., Jang, J., & Kim, Y. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00268 · Cited by 22
Towards the development of an omics data analysis framework
Verheijen, M. C. T., Tong, W., Shi, L. M., Gant, T. W., Seligman, B., & Caiment, F. (2020) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2020.104621 · Cited by 24
Wang, Z., Jia, C., Wang, J., Miao, H., Liu, J., Chen, C., Yang, H.-X., Xu, B., & Jin, Z. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00650 · Cited by 22
Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities
Quo, C. F., Kaddi, C. D., Phan, J. H., Zollanvari, A., Xu, M., Wang, M. D., & Alterovitz, G. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbs026 · Cited by 22
Microarray studies in pulmonary arterial hypertension
Menon, S., Fessel, J. P., & West, J. D. (2010) · International Journal of Clinical Practice · doi:10.1111/j.1742-1241.2010.02604.x · Cited by 21
Qian, T., Cui, L., Liu, Y., Cheng, Z., Quan, L., Zeng, T., Huang, W., Dai, Y., Chen, J., Liu, L., et al. (2020) · The Pharmacogenomics Journal · doi:10.1038/s41397-019-0145-6 · Cited by 21
Zhao, S. D., & Li, Y. (2014) · Biometrics · doi:10.1111/biom.12209 · Cited by 20
“These data are described in Zhan et al. (2006) and Shaughnessy et al. (2007), and can be obtained through the MicroArray Quality Control Consortium II study (Shi et al., 2010), available on GEO (GSE24080).”
MethodDREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data
Gao, Z., Zhao, Z., & Tang, W. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00588 · Cited by 22
Artificial Intelligence in Oncology Drug Discovery and Development
Cassidy, J. W., & Taylor, B. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.88376 · Cited by 21
Ali, M. M., Algashamy, H. A. A., Alzidi, E., Ahmed, K., Bui, F. M., Patel, S. K., Azam, S., Abdulrazak, L. F., & Moni, M. A. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100202 · Cited by 20
An effect size filter improves the reproducibility in spectral counting-based comparative proteomics
Gregori, J., Villarreal, L., Sánchez‐Pla, Á., Baselga, J. M., & Villanueva, J. (2013) · Journal of Proteomics · doi:10.1016/j.jprot.2013.05.030 · Cited by 20
Comparison of Feature Selection Methods for Cross-Laboratory Microarray Analysis
Liu, H., Peng, P.-C., Hsieh, T., Yeh, T., Lin, C., Chen, C., Hou, J., Shih, L., & Liang, D. (2013) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2013.70 · Cited by 21
“A recent study of MicroArray Quality Control (MAQC) [21] suggested that the model performance depends largely on the difficulty of the problem, and that the impact of feature selection methods seems less important.”
BackgroundPrediction and prognostic significance of BCAR3 expression in patients with multiple myeloma
Zhang, W., Lin, Y., Liu, X., He, X., Zhang, Y., Fu, W., Yang, Z., Yang, P., Wang, J., Hu, K., et al. (2018) · Journal of Translational Medicine · doi:10.1186/s12967-018-1728-8 · Cited by 19
Cirenajwis, H., Lauss, M., Planck, M., Vallon‐Christersson, J., & Staaf, J. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz008 · Cited by 20
“of model (as also noted previously by MAQC-II for other classifier types [28]).”
MethodIshii, T., Mitsui, T., Suzuki, S., Matsuzaki, Y., & Hasegawa, T. (2012) · Endocrinology · doi:10.1210/en.2011-1627 · Cited by 22
Pharmacogenomic Biomarkers for Personalized Medicine
Chen, J. J., Lin, W., & Chen, H.-C. (2013) · Pharmacogenomics · doi:10.2217/pgs.13.75 · Cited by 20
Qi, T., Qu, J., Tu, C., Lu, Q., Li, G., Wang, J., & Qu, Q. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.596777 · Cited by 19
Park, S., & Yi, G. (2022) · Cancers · doi:10.3390/cancers14040881 · Cited by 19
Count ratio model reveals bias affecting NGS fold changes
Erhard, F., & Zimmer, R. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv696 · Cited by 19
omniBiomarker: A Web-Based Application for Knowledge-Driven Biomarker Identification
Phan, J. H., Young, A. N., & Wang, M. D. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2212438 · Cited by 19
Chen, M., Shi, L. M., Kelly, R. J., Perkins, R., Fang, H., & Tong, W. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s10-s3 · Cited by 19
Expression Signature as a Biomarker for Prenatal Diagnosis of Trisomy 21
Volk, M., Maver, A., Lovrečić, L., Juvan, P., & Peterlin, B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074184 · Cited by 19
Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome
Cornero, A., Acquaviva, M., Fardin, P., Versteeg, R., Schramm, A. R., Eva, A., Bosco, M. C., Blengio, F., Barzaghi, S., & Varesio, L. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s4-s13 · Cited by 19
Integration of multi-modal biomedical data to predict cancer grade and patient survival
Phan, J. H., Hoffman, R., Kothari, S., Wu, P.-Y., & Wang, M. D. (2016) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2016.7455963 · Cited by 18
Eikrem, Ø., Strauss, P., Beisland, C., Scherer, A., Landolt, L. Z., Flatberg, A., Leh, S. M., Beisvåg, V., Skogstrand, T., Hjelle, K. M., et al. (2016) · Scandinavian Journal of Urology · doi:10.1080/21681805.2016.1238007 · Cited by 19
Reproducibility in Biomarker Research and Clinical Development: A Global Challenge
Scherer, A. (2017) · Biomarkers in Medicine · doi:10.2217/bmm-2017-0024 · Cited by 19
Robust boosting with truncated loss functions
Wang, Z. (2018) · Electronic Journal of Statistics · doi:10.1214/18-ejs1404 · Cited by 19
Maggio, V., Chierici, M., Jurman, G., & Furlanello, C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0208924 · Cited by 20
Prognostic model for multiple myeloma progression integrating gene expression and clinical features
Sun, C., Li, H., Mills, R. E., & Guan, Y. (2019) · GigaScience · doi:10.1093/gigascience/giz153 · Cited by 19
“There are 4 cohorts from different sources, GSE24080UAMS [15], HOVON65 [16], EMTAB4032 [17], and MMRF [18].”
Identifying and optimizing human endometrial gene expression signatures for endometrial dating
Díaz-Gimeno, P., Sebastián-León, P., Sánchez-Reyes, J. M., Späth, K., Alemán, A., Vidal, C., Devesa-Peiró, A., Labarta, E., Ribas, I. S., Ferrando, M., et al. (2021) · Human Reproduction · doi:10.1093/humrep/deab262 · Cited by 20
Tillinghast, G. W. (2010) · Nature Biotechnology · doi:10.1038/nbt0810-810 · Cited by 19
Boczek, T., Lisek, M., Ferenc, B., & Żylińska, L. (2017) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2017.01.012 · Cited by 19
Devanarayan, V., Charil, A., Horie, K., Doherty, T., Llano, D. A., Andreozzi, E., Sachdev, P., Ye, Y., Murali, L. K., Zhou, J., et al. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.14411 · Cited by 19
Using pathway modules as targets for assay development in xenobiotic screening
Judson, R., Mortensen, H. M., Shah, I., Knudsen, T. B., & Elloumi, F. (2011) · Molecular BioSystems · doi:10.1039/c1mb05303e · Cited by 18
Xu, L., Fengji, L., Liu, C., Zhang, L., Li, Y., Li, Y., Shanguang, C., & Jianghui, X. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142433 · Cited by 19
AI Slipping on Tiles: Data Leakage in Digital Pathology
Bussola, N., Marcolini, A., Maggio, V., Jurman, G., & Furlanello, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68763-2_13 · Cited by 18
Carollo, A., Bizzego, A., Gabrieli, G., Wong, K. K., Raine, A., & Esposito, G. (2021) · Public Health in Practice · doi:10.1016/j.puhip.2021.100219 · Cited by 20
“The adopted training scheme is standardized and was derived from similar applications on bioinformatics [50] and public health [51].”
Palmisano, A., Vignale, D., Boccia, E., Nonis, A., Gnasso, C., Leone, R., Montagna, M., Nicoletti, V., Bianchi, A. G., Brusamolino, S., et al. (2022) · La radiologia medica · doi:10.1007/s11547-022-01518-0 · Cited by 18
“A three-layered Data Analysis Plan (DAP) extending the 10 × 5 repeated CV design of the FDA-led MAQC-II project [19] was adopted for model development and selection.”
Method
Metadata
- Authors
- MAQC Consortium
- Venue
- Nature Biotechnology
- Published
- 2010-07-30
- Volume
- 28
- Issue
- 8
- Pages
- 827-838
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3315840
- OpenAlex
- W2021560851
- Cited by
- 889
- References
- 63
- Citations by year
- 2026: 13 · 2025: 33 · 2024: 39 · 2023: 35 · 2022: 40 · 2021: 58 · 2020: 61 · 2019: 58 · 2018: 46 · 2017: 35 · 2016: 57 · 2015: 54 · 2014: 87 · 2013: 85 · 2012: 79
- FWCI
- 34.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Institute of Process Engineering, CAS Institutional Repository (submittedVersion, cc-by-nc-sa)
- IRIS (submittedVersion)
- Repository of Futwangen University of Applied Sciences (Furtwangen University) (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- National Institutes of Health (Z99 LM999999, Z99 HL999999, Z99 MH999999, Z99CL999999)
- National Cancer Institute (CA97513-01, CA55819-01A1, R00CA126184, P01CA055819, R33CA097513)
- National Institute of General Medical Sciences (T32GM074906, R01GM083084, R01GM083226, R01GM072611)
- National Center for Research Resources (R01RR021967)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- microarray quality control, gene expression profiling, prediction model, genomic classifier, model validation, pulmonary toxicity, hepatotoxicity, breast cancer, multiple myeloma, neuroblastoma, rodent toxicity, method comparison, reproducibility
- MeSH terms
- Animals, Breast Neoplasms, Disease Models, Animal, Female, Humans, Liver Diseases, Lung Diseases, Multiple Myeloma, Neoplasms, Neuroblastoma, Predictive Value of Tests, Quality Control, Survival Analysis, Guidelines as Topic, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Rats
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
